ean 9780521887939 temática ESTADÍSTICA año Publicación 2013 idioma INGLÉS editorial CAMBRIDGE UP. ESPAÑA formato CARTONÉ 66,55 €    PEDIR   NOVEDAD

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’Big data’ poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed ’safe operating zone’ for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master’s/graduate students in statistics and researchers in data-rich disciplines.

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Part I. Classical Methods: 1. Multidimensional data 2. Principal component analysis 3. Canonical correlation analysis 4. Discriminant analysis Part II. Factors and Groupings: 5. Norms, proximities, features, and dualities 6. Cluster analysis 7. Factor analysis 8. Multidimensional scaling Part III. Non-Gaussian Analysis: 9. Towards non-Gaussianity 10. Independent component analysis 11. Projection pursuit 12. Kernel and more independent component methods 13. Feature selection and principal component analysis revisited Index. AuthorInge Koch, University of Adelaide Inge Koch is Associate Professor of Statistics at the University of Adelaide, Australia. Related Books Kernel Methods for Pattern Analysis Experimental Design and Data Analysis for Biologists Kernel Methods and Machine Learning Machine Learning Methods in the Environmental Sciences Neural Networks and Kernels Topics in Applied Multivariate Analysis Data Analysis and Graphics Using R An Example-based Approach Data Analysis and Graphics Using R An Example-Based Approach Independent Component Analysis Principles and Practice Kernel Methods for Pattern Analysis Experimental Design and Data Analysis for Biologists 12345678 PreviousNext